Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session F21: Boundary Layers: Modeling and Analysis
8:00 AM–9:57 AM,
Monday, November 19, 2018
Georgia World Congress Center
Room: B309
Chair: George Park, University of Pennsylvania
Abstract ID: BAPS.2018.DFD.F21.5
Abstract: F21.00005 : Predictive LES wall modeling via physics-informed neural networks
8:52 AM–9:05 AM
Presenter:
Xiang Yang
(Penn State University)
Authors:
Xiang Yang
(Penn State University)
Suhaib Zafar
(Penn State University)
Jianxun Wang
(Univ of California - Berkeley)
Heng Xiao
(Virginia Tech)
While data-based approaches were found to be useful for sub-grid scale (SGS) modeling in Reynolds-averaged Navier-Stokes (RANS) simulations, there have not been many attempts of using machine learning (ML) techniques for wall modeling in large-eddy simulations (LES). LES wall modeling poses additional challenges to data-based modeling approaches. First, datasets of higher fidelity are not easily accessible. Second, wall modeling needs to account for both near-wall small scales and large scales above the wall. In this work, we discuss how the above-noted challenges may be addressed. We will also show the necessity of incorporating physics insights in model inputs, i.e. using inputs that are inspired by the vertically integrated thin boundary layer equations and the eddy population density scalings. We will show that the inclusion of above physics-based considerations would enhance extrapolation capabilities of a neural network to flow conditions that are not within the train data. Being cheap-to-evaluate and using only channel flow data at Re_\tau=1,000, the trained networks are found to capture the law of the wall at arbitrary Reynolds numbers and outperform the conventional equilibrium model in a non-equilibrium flow.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.DFD.F21.5
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